code: def _create_subsets_and_weights_approximation(
      num_players: int, high_weight: float, num_subset_samples: int
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Randomly samples subsets and weights them based on the number of how often they appear.
  
      :param num_players: Total number of players.
      :param high_weight: A 'high' weight for computational purposes. This is used to resemble 'infinity', but needs to be
                          selected carefully to avoid numerical issues.
      :param num_subset_samples: Number of subset samples.
      :return: A tuple, where the first entry is a numpy array with the sampled subsets and the second entry is an array
               with the corresponding weights to each subset.
      """
      all_subsets = [np.zeros(num_players), np.ones(num_players)]
      weights = {
          tuple(all_subsets[0]): high_weight,
          tuple(all_subsets[1]): high_weight,
      }
  
      probabilities_of_subset_length = np.zeros(num_players + 1)
      for i in range(1, num_players):
          probabilities_of_subset_length[i] = (num_players - 1) / (
              i * (num_players - i)
          )
  
      probabilities_of_subset_length = (
          probabilities_of_subset_length
          / np.sum(probabilities_of_subset_length)
      )
  
      for i in range(num_subset_samples):
          subset_as_tuple = (
              _convert_list_of_indices_to_binary_vector_as_tuple(
                  np.random.choice(
                      num_players,
                      np.random.choice(
                          num_players + 1,
                          1,
                          p=probabilities_of_subset_length,
                      ),
                      replace=False,
                  ),
                  num_players,
              )
          )
  
          if subset_as_tuple not in weights:
              weights[subset_as_tuple] = 0
              all_subsets.append(np.array(subset_as_tuple))
  
          weights[subset_as_tuple] += 1
  
      weights = np.array([weights[tuple(x)] for x in all_subsets])
  
      return np.array(all_subsets, dtype=np.int32), weights.astype(
          float
      )
  
imports: import itertools
  import math
  from enum import Enum
  from typing import Callable, Dict, List, Optional, Set, Tuple, Union
  import numpy
  import scipy
  from joblib import Parallel, delayed
  from scipy.special import comb
  from scipy.stats._qmc import Halton
  from sklearn.linear_model import LinearRegression
  from tqdm import tqdm
  import dowhy.gcm.config
  from dowhy.gcm.util.general import set_random_seed
label_desc: Use image node_id 8 for calling a global function with example usage: _create_subsets_and_weights_approximation(num_players, high_weight, num_subset_samples) and returns: np,  weights